Deploying models with dstack

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dstack is an alternative to Kubernetes for the orchestration of AI and ML applications. With dstack, you can use YAML configuration files to define the Lambda Public Cloud resources needed for your applications. dstack will automatically obtain those resources, that is, launch appropriate on-demand instances, and start your applications.

In this tutorial, you'll learn how to set up dstack, and use it to deploy vLLM on a Lambda Public Cloud on-demand instance. vLLM will serve the Hermes 3 fine-tuned Llama 3.1 8B large language model (LLM).

All of the instructions in this tutorial should be followed on your computer. This tutorial assumes you already have installed:

• python3

• python3-venv

• pip

• git

• curl

• jq

You can install these packages by running:

sudo apt update && sudo apt install -y python3 python3-venv pip git curl jq

Setting up the dstack server

To set up the dstack server:

1. Generate a Cloud API key from the Lambda Public Cloud dashboard.

2. Create a directory for this tutorial, and change into the directory by running:

mkdir ~/lambda-dstack-tutorial && cd ~/lambda-dstack-tutorial

Then, create and activate a Python virtual environment by running:

python3 -m venv .venv && source .venv/bin/activate

3. Install dstack by running:

pip install -U "dstack[all]"

4. Create a directory for the dstack server and change into the directory by running:

mkdir -p -m 700 ~/.dstack/server && cd ~/.dstack/server

In this directory, create a configuration file named config.yml with the following contents:

projects:
- name: main
  backends:
    - type: lambda
      creds:
        type: api_key
        api_key: API-KEY

Replace API-KEY with your actual Cloud API key.

Then, start the dstack server by running:

dstack server

You should see output similar to:

[16:20:35] INFO     Applying ~/.dstack/server/config.yml...
[16:20:36] INFO     The admin token is ADMIN-TOKEN
           INFO     The dstack server 0.18.11 is running at http://127.0.0.1:3000

Deploying vLLM and serving Hermes 3

To deploy vLLM and serve the Hermes 3 model:

5. Open another terminal. Then, change into the directory you created for this tutorial, and activate the Python virtual environment you created earlier, by running:

cd ~/lambda-dstack-tutorial && source .venv/bin/activate

6. In this directory, create a new directory named task-hermes-3-vllm and change into the new directory by running:

mkdir task-hermes-3-vllm && cd task-hermes-3-vllm

In this new directory, create a filed named .dstack.yml with the following contents:

type: task
name: task-hermes-3-vllm
python: "3.10"
env:
  - MODEL=NousResearch/Hermes-3-Llama-3.1-8B
commands:
  - pip install vllm
  - vllm serve $MODEL
ports:
  - 8000
spot_policy: auto
resources:
  gpu: 40GB..80GB

Then, initialize and apply the configuration by running:

dstack init && dstack apply

7. You'll see output similar to:

 Configuration          .dstack.yml
 Project                main
 User                   admin
 Pool                   default-pool
 Min resources          2..xCPU, 8GB.., 1xA100, 100GB.. (disk)
 Max price              -
 Max duration           72h
 Spot policy            auto
 Retry policy           no
 Creation policy        reuse-or-create
 Termination policy     destroy-after-idle
 Termination idle time  5m

 #  BACKEND  REGION      INSTANCE          RESOURCES                                     SPOT  PRICE
 1  lambda   us-south-1  gpu_1x_a100       30xCPU, 215GB, 1xA100 (40GB), 549.9GB (disk)  no    $1.29
 2  lambda   us-west-2   gpu_1x_a100_sxm4  30xCPU, 215GB, 1xA100 (40GB), 549.9GB (disk)  no    $1.29
 3  lambda   us-east-1   gpu_1x_a100_sxm4  30xCPU, 215GB, 1xA100 (40GB), 549.9GB (disk)  no    $1.29
    ...
 Shown 3 of 20 offers, $1.29 max

Submit the run task-vllm-hermes-3? [y/n]:

Press Y then Enter to submit the run. The run will take several minutes to complete.

dstack will automatically:

• Launch an instance with between 40GB and 80GB of VRAM

• Install vLLM and its dependencies using pip

• Download the Hermes 3 model

• Start vLLM and serve the Hermes 3 model

You're billed for all of the time the instance is running.

In the Lambda Public Cloud dashboard, you can see the instance launching.

vLLM is running and serving the Hermes 3 model once you see output similar to:

INFO:     Started server process [57]
INFO:     Waiting for application startup.
INFO:     Application startup complete.
INFO:     Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)

8. In another terminal, test that vLLM is serving the Hermes 3 model by running:

curl -X POST http://localhost:8000/v1/completions \
     -H "Content-Type: application/json" \
     -d "{
           \"prompt\": \"What is the name of the capital of France?\",
           \"model\": \"NousResearch/Hermes-3-Llama-3.1-8B\",
           \"temperature\": 0.0,
           \"max_tokens\": 1
         }" | jq .

You should see output similar to:

{
  "id": "cmpl-250265add39147f58e7dba91ab244c8b",
  "object": "text_completion",
  "created": 1724824435,
  "model": "NousResearch/Hermes-3-Llama-3.1-8B",
  "choices": [
    {
      "index": 0,
      "text": " Paris",
      "logprobs": null,
      "finish_reason": "length",
      "stop_reason": null,
      "prompt_logprobs": null
    }
  ],
  "usage": {
    "prompt_tokens": 11,
    "total_tokens": 12,
    "completion_tokens": 1
  }
}

9. To quit vLLM and terminate the instance that was launched:

In the previous terminal, that is, the terminal you used to run dstack apply, press Ctrl + C. You'll be asked if you want to stop the run before detaching. Press Y then Enter. You'll see Stopped once the run is stopped.

After 5 minutes, the instance will terminate. Alternatively, you can delete the instance immediately by running:

dstack fleet delete task-hermes-3-vllm

You'll be asked for confirmation that you want to delete the fleet, that is, the instance launched for this tutorial. Press Y then press Enter.

Using the Lambda Public Cloud dashboard, you can confirm that the instance was terminated.

10. To shut down the dstack server, press Ctrl + C in the terminal running the server.